Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis

A computationally scalable, statistically efficient aggregate-disaggregate data fusion method that corrects for selection bias is applied to model customer relationship dynamics at a subscription-based firm.

The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis

Context matters when modeling customer purchases and attrition in noncontractual settings.

COMPARATIVE MODELS IN CUSTOMER BASE ANALYSIS: PARAMETRIC MODEL AND OBSERVATION-DRIVEN MODEL

This study conducts a dynamic rolling comparison between the Pareto/NBD model (parametric model) and machine learning algorithms (observation-driven models) in customer base analysis, which the literature has not comprehensively investigated before. The aim is to find the comparative edge of these two approaches under customer base analysis and to define the implementation timing of these two paradigms. This research utilizes Pareto/NBD (Abe) as representative of Buy-Till-You-Die (BTYD) models in order to compete with machine learning algorithms and presents the following results. (1) The parametric model wins in transaction frequency prediction, whereas it loses in inactivity prediction. (2) The BTYD model outperforms machine learning in inactivity prediction when the customer base is active, performs better in an inactive customer base when competing with Poisson regression, and wins in a short-term active customer base when competing with a neural network algorithm in transaction frequency prediction. (3) The parametric model benefits more from a short calibration length and a long holdout/target period, which exhibit uncertainty. (4) The covariate effect helps Pareto/NBD (Abe) gain a better predictive result. These findings assist in defining the comparative edge and implementation timing of these two approaches and are useful for modeling and business decision making.